'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={"vocab_file": "sentencepiece.bpe.model"}

__UpperCAmelCase					={
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__UpperCAmelCase					={
    "moussaKam/mbarthez": 1_0_2_4,
    "moussaKam/barthez": 1_0_2_4,
    "moussaKam/barthez-orangesum-title": 1_0_2_4,
}

__UpperCAmelCase					="▁"




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Dict				=VOCAB_FILES_NAMES
     lowerCamelCase    :	List[Any]				=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase    :	int				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase    :	List[Any]				=["input_ids", "attention_mask"]






     def __init__(					self						:						Tuple    ,							a						:						Dict    ,							a						:						List[str]="<s>"    ,							a						:						List[Any]="</s>"    ,							a						:						Optional[Any]="</s>"    ,							a						:						Dict="<s>"    ,							a						:						Optional[Any]="<unk>"    ,							a						:						Dict="<pad>"    ,							a						:						Dict="<mask>"    ,							a						:						Optional[Dict[str, Any]] = None    ,							**a						:						List[str]    ,							):





         """simple docstring"""



         __lowerCamelCase            =    AddedToken(a    ,							lstrip=a    ,							rstrip=a       ) if isinstance(a    ,							a       ) else mask_token

         __lowerCamelCase            =    {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=a    ,							eos_token=a    ,							unk_token=a    ,							sep_token=a    ,							cls_token=a    ,							pad_token=a    ,							mask_token=a    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a    ,							)

         __lowerCamelCase            =    vocab_file
         __lowerCamelCase            =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(str(a       )       )

         __lowerCamelCase            =    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         __lowerCamelCase            =    len(self.sp_model       ) - 1
         __lowerCamelCase            =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						List[int]    ,							a						:						Optional[List[int]] = None       ):





         """simple docstring"""



         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __lowerCamelCase            =    [self.cls_token_id]
         __lowerCamelCase            =    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






     def   SCREAMING_SNAKE_CASE__  (					self						:						str    ,							a						:						List[int]    ,							a						:						Optional[List[int]] = None    ,							a						:						bool = False       ):





         """simple docstring"""



         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=a    ,							token_ids_a=a    ,							already_has_special_tokens=a       )

         if token_ids_a is None:
             return [1] + ([0] * len(a       )) + [1]
         return [1] + ([0] * len(a       )) + [1, 1] + ([0] * len(a       )) + [1]






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							a						:						List[int]    ,							a						:						Optional[List[int]] = None       ):





         """simple docstring"""



         __lowerCamelCase            =    [self.sep_token_id]
         __lowerCamelCase            =    [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         return len(self.sp_model       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    {self.convert_ids_to_tokens(a       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						str       ):





         """simple docstring"""



         return self.sp_model.encode(a    ,							out_type=a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						Any       ):





         """simple docstring"""



         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         __lowerCamelCase            =    self.sp_model.PieceToId(a       )

         return spm_id if spm_id else self.unk_token_id






     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						Tuple       ):





         """simple docstring"""



         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    []
         __lowerCamelCase            =    ''''''
         __lowerCamelCase            =    False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 if not prev_is_special:
                     out_string += " "
                 out_string += self.sp_model.decode(a       ) + token
                 __lowerCamelCase            =    True
                 __lowerCamelCase            =    []
             else:
                 current_sub_tokens.append(a       )
                 __lowerCamelCase            =    False
         out_string += self.sp_model.decode(a       )
         return out_string.strip()






     def __getstate__(					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.__dict__.copy()
         __lowerCamelCase            =    None
         return state






     def __setstate__(					self						:						Tuple    ,							a						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    d

         # for backward compatibility
         if not hasattr(self    ,							'''sp_model_kwargs'''       ):
             __lowerCamelCase            =    {}

         __lowerCamelCase            =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(self.vocab_file       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						str    ,							a						:						Optional[str] = None       ):





         """simple docstring"""



         if not os.path.isdir(a       ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
             return
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

         if os.path.abspath(self.vocab_file       ) != os.path.abspath(a       ) and os.path.isfile(self.vocab_file       ):
             copyfile(self.vocab_file    ,							a       )
         elif not os.path.isfile(self.vocab_file       ):
             with open(a    ,							'''wb'''       ) as fi:
                 __lowerCamelCase            =    self.sp_model.serialized_model_proto()
                 fi.write(a       )

         return (out_vocab_file,)

'''simple docstring'''




from manim import *




class        a__ (  UpperCAmelCase__					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    Rectangle(height=0.5    ,							width=0.5       )
         __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0       )

         __lowerCamelCase            =    Rectangle(height=0.25    ,							width=0.25       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''CPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         cpu.move_to([-2.5, -0.5, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(4       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''GPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         gpu.move_to([-1, -1, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )

         __lowerCamelCase            =    Text('''Model'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         model.move_to([3, -1.0, 0]       )
         self.add(a       )

         __lowerCamelCase            =    []
         __lowerCamelCase            =    []

         for i, rect in enumerate(a       ):
             __lowerCamelCase            =    fill.copy().set_fill(a    ,							opacity=0.8       )
             target.move_to(a       )
             model_arr.append(a       )

             __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0.0       ).set_fill(a    ,							opacity=0.8       )
             cpu_target.move_to(cpu_left_col_base[i]       )
             model_cpu_arr.append(a       )

         self.add(*a    ,							*a       )

         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''Disk'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         disk.move_to([-4, -1.25, 0]       )
         self.add(a    ,							a       )

         __lowerCamelCase            =    Square(side_length=2.2       )
         key.move_to([-5, 2, 0]       )

         __lowerCamelCase            =    MarkupText(
             f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""    ,							font_size=18    ,							)

         key_text.move_to([-5, 2.4, 0]       )

         self.add(a    ,							a       )

         __lowerCamelCase            =    MarkupText(
             f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""    ,							font_size=18    ,							)

         blue_text.next_to(a    ,							DOWN * 2.4    ,							aligned_edge=key_text.get_left()       )
         self.add(a       )

         __lowerCamelCase            =    MarkupText(
             f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a       )       )

         __lowerCamelCase            =    Square(0.3       )
         input.set_fill(a    ,							opacity=1.0       )
         input.set_stroke(width=0.0       )
         input.next_to(model_base[0]    ,							a    ,							buff=0.5       )

         self.play(Write(a       )       )

         input.generate_target()
         input.target.next_to(model_arr[0]    ,							direction=a    ,							buff=0.02       )
         self.play(MoveToTarget(a       )       )

         self.play(FadeOut(a       )       )

         __lowerCamelCase            =    Arrow(start=a    ,							end=a    ,							color=a    ,							buff=0.5       )
         a.next_to(model_arr[0].get_left()    ,							a    ,							buff=0.2       )

         model_cpu_arr[0].generate_target()
         model_cpu_arr[0].target.move_to(gpu_rect[0]       )

         __lowerCamelCase            =    MarkupText(
             f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )       )

         __lowerCamelCase            =    {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}

         self.play(
             Write(a       )    ,							Circumscribe(model_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_cpu_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)
         self.play(MoveToTarget(model_cpu_arr[0]       )       )

         __lowerCamelCase            =    a.copy()
         for i in range(6       ):
             a_c.next_to(model_arr[i].get_right() + 0.02    ,							a    ,							buff=0.2       )

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02       )

             __lowerCamelCase            =    AnimationGroup(
                 FadeOut(a    ,							run_time=0.5       )    ,							MoveToTarget(a    ,							run_time=0.5       )    ,							FadeIn(a    ,							run_time=0.5       )    ,							lag_ratio=0.2       )

             self.play(a       )

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]       )

             if i < 5:
                 model_cpu_arr[i + 1].generate_target()
                 model_cpu_arr[i + 1].target.move_to(gpu_rect[0]       )
                 if i >= 1:
                     __lowerCamelCase            =    0.7

                 self.play(
                     Circumscribe(model_arr[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i + 1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_arr[i + 1]    ,							color=a    ,							**a       )    ,							)
                 if i < 1:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]       )    ,							MoveToTarget(model_cpu_arr[i + 1]       )    ,							)
                 else:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]    ,							run_time=0.7       )    ,							MoveToTarget(model_cpu_arr[i + 1]    ,							run_time=0.7       )    ,							)
             else:
                 model_cpu_arr[i].generate_target()
                 model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]       )
                 input.generate_target()
                 input.target.next_to(model_arr[-1].get_right()    ,							RIGHT + 0.02    ,							buff=0.2       )

                 self.play(
                     Circumscribe(model_arr[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(cpu_left_col_base[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)

                 self.play(MoveToTarget(model_cpu_arr[i]       )       )

             __lowerCamelCase            =    a_c
             __lowerCamelCase            =    a_c.copy()

         input.generate_target()
         input.target.next_to(model_base[-1]    ,							RIGHT + 0.02    ,							buff=0.5       )
         self.play(
             FadeOut(a       )    ,							FadeOut(a    ,							run_time=0.5       )    ,							)

         __lowerCamelCase            =    MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""    ,							font_size=24       )
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )    ,							MoveToTarget(a       )       )

         self.wait()
